Monthly rainfall prediction using wavelet regression and neural network: an analysis of 1901–2002 data,Assam, India

Rainfall is a principal element of the hydrological cycle and its variability is important from both the scientific as well as practical point of view. Wavelet regression (WR) technique is proposed and developed to analyze and predict the rainfall forecast in this study. The WR model is improved combining two methods, discrete wavelet transform and linear regression model. This study uses rainfall data from 21 stations in Assam, India over 102 years from 1901 to 2002. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The root mean square errors (RMSE), N-S index, and correlation coefficient (R) statistics were used for evaluating the accuracy of the WR models. The accuracy of the WR models was then compared with those of the artificial neural networks (ANN) models. The results of monthly rainfall series modeling indicate that the performances of wavelet regression models are found to be more accurate than the ANN models.     

Satellite-based technique for nowcasting of thunderstorms over Indian region

India experiences severe thunderstorms during the months, March–June. But these systems are not predicted well, mainly due to the absence of mesoscale observational network over Indian region and the expert system. As these are short lived systems, the nowcast is attempted worldwide based on satellite and radar observations. Due to inadequate radar network, satellite plays the dominant role for nowcast of these thunderstorms. In this study, a nowcast based algorithm ForTracc developed by Vila et al. (Weather Forecast 23:233–245, 2008) has been examined over the Indian region using Infrared Channel \((10.8~\upmu \hbox {m})\) of INSAT-3D for prediction of Mesoscale Convective Systems (MCS). In this technique, the current location and intensity in terms of Cloud Top Brightness Temperature (CTBT) of the MCS are extrapolated. The purpose of this study is to validate this satellite-based nowcasting technique for Convective Cloud Clusters that helps in optimum utilization of satellite data and improve the nowcasting. The model could predict reasonably the minimum CTBT of the convective cell with average absolute error (AAE) of \({<}7\hbox { K}\) for different lead periods (30–180 min). However, it was underestimated for all the lead periods of forecasts. The AAE in the forecasts of size of the cluster varies from about \(3\times 10^{4}\hbox { km}^{2}\) for 30-min forecast to \(7\times 10^{4}\hbox { km}^{2}\) for 120-min forecast. The mean absolute error in prediction of size is above 31–38% of actual size for different lead periods of forecasts from 30 to 180 min. There is over estimation in prediction of size for 30 and 60 min forecasts (17% and 2.6% of actual size of the cluster, respectively) and underestimation in 90 to 180-min forecasts (–2.4% to –28%). The direct position error (DPE) based on the location of minimum CTBT ranges from 70 to 144 km for 30–180-min forecast respectively.